DTA

Corso Ordinario Secondo Livello

BOUCHS, ALESSANDRO

etd-11042021-215232

Forecasting Financial Volatility with High Dimensional Data: A Deep Learning Approach

Cl. Sc. Sociali - Scienze Economiche

SCIENZE ECONOMICHE E MANAGERIALI - SCIENZE ECONOMICHE E MANAGERIALI

relatore Prof. MARIA ENRICA VIRGILLITO
Relatore Prof. RAGUSA, GIUSEPPE
Presidente Prof. BARONTINI, ROBERTO
Membro Prof. MINA, ANDREA
Membro Prof. CINQUINI, LINO
Membro Dott. VANDIN, ANDREA

• deep learning
• machine learning
• neural networks
• realized volatility

01/12/2021;

parziale

This project tackles the issue of dimension reduction and forecasting with high dimensional financial data. The most common dimension reduction technique is Principal Component Analysis, which forms the backbone of Dynamic Factor Models and is based on linear transformations of the data. Yet, scholars have argued that non-linear structures in the data could be exploited to obtain an improved representation of the original data and more accurate forecasts. In this project, I experiment the use of neural networks, part of the arsenal of techniques coming from deep learning, as a dimension reduction method that exploits non-linear interactions. Using short-term signals from the order book data of 107 stocks observed in 3380 10-minute windows, I derive a measure of realized volatility for each window. This forms the basis to estimate factors by Principal Component Analysis and also by training several autoencoders with differing depth. Subsequently, I generate factor-augmented forecasts of the volatility of a number of stocks in the following 10-minute windows and compare the results in order to assess both methodologies against one another and against traditional models used in the literature.

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